Advanced Interdisciplinary Applications Of Deep Learning For Data Science
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Advanced Interdisciplinary Applications of Deep Learning for Data Science
Advanced interdisciplinary applications of deep learning are revolutionizing the field of data science, enabling new solutions across industries. By leveraging neural networks and datasets, deep learning techniques are transforming how businesses analyze, interpret, and gain insights from complex data. In fields like healthcare, finance, engineering, and business, deep learning models can uncover patterns and make predictions to enhance data-driven decision-making while fostering new opportunities in areas like personalized medicine, autonomous systems, and predictive analytics. As deep learning continues evolves, its interdisciplinary impact expands, creating new possibilities for solving challenges in data science. Advanced Interdisciplinary Applications of Deep Learning for Data Science explores innovations of deep learning technology in data science for various industries and sectors. It examines solutions for decision-making and patient or customer service using machine learning, AI, and predictive analytics. This book covers topics such as bioinformatics, environmental monitoring, and medical diseases, and is a useful resource for data scientists, computer engineers, medical professionals, academicians, and researchers.
Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts to use efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and code in Python with all needed libraries and links to datasets used. Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, k-nearest neighbor, market basket analysis, Apriori, k-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners.
Artificial Intelligence in Human Health and Diseases
This book discusses current concepts and future translational possibilities of Artificial Intelligence (AI) in human healthcare and diseases. The initial chapter reviews the application and integration of AI and multi-omics to develop novel diagnostic and therapeutic strategies. The book chapter also explores the recent advances in data-driven approaches that aided state-of-the-art AI for biomarker discovery and better disease diagnosis. The book also offers computational and oncological prospects for the role of AI in radio genomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. The chapters further cover the role of the current state of AI in Cardiovascular Disease, Obesity and Diabetes, Neurological Diseases, and Infectious Diseases. Towards the end, the book highlights the use of AI in diverse pharmaceutical industry sectors, including drug discovery and development, drug repurposing, and improving pharmaceutical productivity. This book is handy for basic, translational, clinical & interdisciplinary researchers and bioinformaticians.